Detecting street parked vehicles

ABSTRACT

Aspects of the disclosure relate to an autonomous vehicle that may detected other nearby vehicles and identify them as parked or unparked. This identification may be based on visual indicia displayed by the detected vehicles as well as traffic control factors relating to the detected vehicles. Detected vehicles that are in a known parking spot may automatically be identified as parked. In addition, detected vehicles that satisfy conditions that are indications of being parked may also be identified as parked. The autonomous vehicle may then base its control strategy on whether or not a vehicle has been identified as parked or not.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/939,468, filed Mar. 29, 2018, which is a continuation ofU.S. patent application Ser. No. 15/384,841, filed Dec. 20, 2016, issuedas U.S. Pat. No. 9,964,954 on May 8, 2018, which is a continuation ofU.S. patent application Ser. No. 14/681,424, filed Apr. 8, 2015 issuedas U.S. Pat. No. 9,557,736 on Jan. 31, 2017, the disclosure of which isincorporated herein by reference.

BACKGROUND

The general behavior of an autonomous vehicle is to stay in its lane andkeep a safe distance from the vehicle in front of it. However, thisassumes that the lead vehicle is moving as part of traffic.

BRIEF SUMMARY

In the case of vehicles parked on the street (e.g., not in parkingspaces physically separated from a lane of travel), it is necessary tomake the distinction between parked and unparked vehicles to preventtreating a vehicle that is not going to move in the near future as partof moving traffic. The autonomous vehicle may then determine the propercontrol strategy based on the distinction (e.g., yielding to an unparkedcar, but navigating around a parked car).

It is important that this distinction be made with high accuracy. Everyfalse negative (i.e., a parked vehicle marked as unparked) is a chanceto get stuck, and every false positive (i.e., an unparked vehicle markedas parked) may result in trying to cut off a moving vehicle. Thisdistinction must also be made with low latency. Waiting too long to calla vehicle parked may result in unnecessary braking, and waiting too longto stop calling a vehicle parked could result in a collision or a nearmiss.

One aspect of the disclosure provides a method for identifying parkedvehicles. The method includes detecting, by one or more computingdevices, a first set of vehicles; identifying, by the one or morecomputing devices, a second set of vehicles, the vehicles in the secondset being one or more vehicles in the first set having zero velocity;detecting, by the one or more computing devices, one or more visibleindicia of the second set of vehicles; identifying, by the one or morecomputing devices, one or more traffic control factors relating to thesecond set of vehicles; identifying, by the one or more computingdevices, a third set of vehicles as parked, the vehicles in the thirdset being one or more vehicles in the second set that are located in aparking spot; identifying, by the one or more computing devices, afourth set of vehicles as parked, the vehicles in the fourth set beingone or more vehicles in the second set but not in the third set that,for a length of time, satisfies two or more of a plurality of conditionsindicative of being parked; and controlling, by the one or morecomputing devices, an autonomous vehicle in accordance with a controlstrategy corresponding to an identification of a vehicle in the firstset. In one example, the first length of time is determined according toan estimated speed of each vehicle prior to having zero velocity.

In another example, a vehicle is determined to be located in a parkingspot if it is at least: (i) located in a traffic control locationassociated with being parked, or (ii) within a first distance from aside of a road and outside a second distance from an intersection. Inthis example, the first distance is determined according to a width ofthe road and the second distance is determined according to one or bothof traffic control lines and traffic control signs.

In yet another example, the plurality of conditions indicative of beingparked comprises at least one of: (1) being at a traffic controllocation associated with being parked, (2) being within a third distancefrom a side of a road, (3) being outside a fourth distance from anintersection, (4) having zero velocity for a second length of time, (5)lacking visual indicia for hazard lights, brake lights, reverse lights,and turn signals, (6) facing a first angle in relation to a side of aroad, (7) having a first width of space on a side of a vehicle furthestfrom a side of a road, (8) being at a type of intersection that does notrequire slowing or stopping, (9) being in a group of vehicles havingzero velocity and being within the third distance and at the first anglefrom a side of a road, and (10) having no change in a size of a portionof a vehicle detected by the one or more computing devices. In thisexample, the third distance is determined according to a width of aroad; the fourth distance is determined according to one or both oftraffic control lines and traffic control signs; the second length oftime is determined according to an estimated speed of a vehicle prior tohaving zero velocity; and the first width of space is determinedaccording to an estimated width of a vehicle. Furthermore, in thisexample, the third distance, the fourth distance, the second length oftime, and the first width of space are ranges of values. Also in thisexample, the first angle is an angle or a range of angles between 0° and90°.

In yet another example, the method of identifying parked vehicles alsoincludes updating, by the one or more computing devices, vehicles in thesecond set of vehicles continuously; updating, by the one or morecomputing devices, one or more of the visual indicia of the second setof vehicles continuously; and identifying, using the one or morecomputing devices, the vehicles in the second set as parked according tothe updated visual indicia.

In still another example, the detection of the one or more visibleindicia includes identifying one or more lights emitted from a vehicle.In this example, the one or more lights comprises at least one of thefollowing: turn signals, hazard lights, emergency vehicle lights, andconstruction warning lights. In addition, the identification of avehicle as possibly parked corresponding to the one or more lights.

Another aspect of the disclosure provides a system that includes anautonomous vehicle and one or more computing devices. This system isconfigured to perform the methods set forth above. Yet another aspect ofthe disclosure provides a non-transitory computer-readable medium onwhich instructions are stored, the instructions, when executed by one ormore computing devices cause the one or more computing devices toperform the disclosed methods for identifying parked vehicles.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of a system in accordance with aspects ofthe disclosure.

FIG. 2 is an interior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 3 is an example of detailed map information in accordance withaspects of the disclosure.

FIG. 4 is an exterior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 5 is an example diagram of a section of roadway including anintersection in accordance with aspects of the disclosure.

FIG. 6 is another example diagram of a section of roadway including anintersection in accordance with aspects of the disclosure.

FIG. 7 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION

Overview

An autonomous vehicle may determine whether a detected stationaryvehicle is parked or part of the traffic flow. Vehicles that are indelineated parking spaces or against street curbs may be identified asparked. Additionally, vehicles that satisfy a number of other conditionsmay be labeled as possibly parked, tracked for a period of time, thenlabeled as parked if the conditions remain consistent with a parkedvehicle. For example, if a car is stopped close to a curb, with aheading parallel to the street, with enough space on the other side forother vehicles to pass by, in a location where street parking isallowed, in a row of other vehicles similarly stopped, the system maydetermine that the car is parallel parked and label it as such. On theother hand, if a car is stopped close to an intersection where there isa stop sign, only for a short period of time, with a turn signal on,where the curb is painted red or yellow, the system may determine thatthe car is waiting to turn and therefore may label the car as notparked. Identifying a vehicle as parked may indicate that it is likelynot going to move anytime soon and therefore the autonomous vehiclewould have time to take actions to avoid the parked vehicle, such aspassing or maneuvering around the parked vehicle. Meanwhile, when avehicle is not identified as parked, the autonomous vehicle may treatthe vehicle as part of traffic and/or monitor the movements of thevehicle more closely so as to be able to respond to its movementsproperly. For example, the autonomous vehicle may stop behind it andwait for it to move.

After detecting surrounding objects and tracking their movements, anautonomous vehicle may identify objects that are capable of moving butare currently stationary. These objects may be other vehicles on theroad. The autonomous vehicle may also determine the type of stationarystate of the detected vehicles. In accordance with one aspect of thedisclosure, the types of stationary states may include a short-termstationary state and a long-term stationary state. The short-termstationary state may indicate that the vehicle is not parked andtherefore likely to begin moving in the near future, while the long-termstationary state may indicate that the vehicle is parked and not likelyto begin moving anytime soon.

From the detected stationary vehicles, an autonomous vehicle mayidentify one or more vehicles that have any visible indicia associatedwith a particular type of stationary state. The autonomous vehicledetect and identify lights such as hazard lights, emergency vehiclelights, construction warning lights, turn signals, brake lights, andreverse lights. Each type of lights may be associated with a parked orunparked state, and therefore may be used in this determination.

In addition, the autonomous vehicle may identify one or more trafficcontrol factors relating to one or more of the vehicles. The autonomousvehicle may compare the location of detected stationary vehicles withmap data such as type of roadway, type of intersection, lane of travel,location of parking spaces, or other traffic pattern. The map data mayalso include the location of parking spaces and no park zones, the colorof the curb, city ordinances regarding parking, or other informationrelated to parking. This information included in the map data may alsobe associated with a vehicle based on its determined location.

If the autonomous vehicle determines that one or more detected vehiclesare located in a designated parking space, the autonomous vehicle maydetermine that the vehicle is in a parked state and labeled as parked inthe system. Designated parking spaces may be parking spaces or parkinglots delineated on the map data. Designated parking spaces may alsoinclude areas where curbside parking is permitted, which are usuallywithin a certain distance from the curb and a certain distance from anintersection. The acceptable distance away from an intersection to parkranges depending on the layout of the intersection. For example,indicators such as the type of line delineating the shoulder and thecolor of the curb may be used to determine what the acceptable distanceis for a particular intersection. City ordinances relevant to parkingareas may also be used for the determination.

Detected vehicles that are not in a designated parking space may stillbe labeled as parked if they display characteristics of a parked vehiclefor a period of time. The detected vehicles may be in a paved or unpavedshoulder, a bike lane, or other locations where vehicles are likelyparked. These vehicles may be marked as possibly parked if they haveother characteristics of a parked vehicle. If the vehicles remainspossibly parked, for example if it continues to have the characteristicsof a parked vehicle, for a set amount of time, it may then be identifiedas parked. The system may set the number of conditions be met and therequired length of time for these conditions to remain unchanged.

These vehicles that are determined to meet the required number ofconditions are tracked to ensure that the state of the vehicle and itssurroundings remains consistent with the original determination for therequired time period. If a change is detected in the state of thevehicle and/or its surroundings within the time period, the system mayagain determine whether or not the detected vehicle is still parked,possibly parked, or unparked based on the new conditions.

If the requisite time period elapses without a change to the possiblyparked status, the detected vehicle may be labeled as parked. Similar towhen vehicles are being monitored after satisfying the conditionsmentioned above, the parked vehicles are continually tracked to detectany changes in the visual indicia or the conditions. If a certain amountof changes occur with respect to a parked vehicle, the parked label maybe removed from the detected vehicle in exchange for an unparked label.This may involve reevaluating whether or not the vehicles satisfy theconditions of a parked vehicle. In addition, the parking statushistories of parked vehicles may be used to assess whether the detectedchanges are significant enough to warrant a change in status. Forexample, if a detected vehicle was first labeled as possibly parked,then labeled as parked for a second or two, a relatively small movementafterwards may not be enough to change the label of the vehicle tounparked. A vehicle with such a history that starts to move is likelyadjusting its parking job.

Depending on the identification detected vehicles or change thereof, anautonomous vehicle may be directed to react accordingly. The featuresdescribed above may allow for an autonomous vehicle to properly identifystationary vehicles that are parked and therefore may be passed. Theymay also quickly identify when a previously parked vehicle is no longerparked and therefore needs to be treated differently than a parkedvehicle. As such, these features may be especially useful for anautonomous vehicle that can navigate the streets without incidence orassistance. Namely, they would be useful to avoid false negatives andfalse positives. By using this method, the autonomous vehicle would notstop behind a parked vehicle and will stop for unparked vehicles.

Example Systems

As shown in FIG. 1 , a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, boats, airplanes,helicopters, lawnmowers, recreational vehicles, amusement park vehicles,farm equipment, construction equipment, trams, golf carts, trains, andtrolleys. The vehicle may have one or more computing devices, such ascomputing device 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including data 132 and instructions 134 that may beexecuted or otherwise used by the processor(s) 120. The memory 130 maybe of any type capable of storing information accessible by theprocessor(s), including a computing device-readable medium, or othermedium that stores data that may be read with the aid of an electronicdevice, such as a hard-drive, memory card, ROM, RAM, DVD or otheroptical disks, as well as other write-capable and read-only memories.Systems and methods may include different combinations of the foregoing,whereby different portions of the instructions and data are stored ondifferent types of media.

The data 132 may be retrieved, stored or modified by processor(s) 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computing device registers, in a relational database asa table having a plurality of different fields and records, XMLdocuments or flat files. The data may also be formatted in any computingdevice-readable format.

The instructions 134 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “instructions” and “programs” may be used interchangeably herein.The instructions may be stored in object code format for directprocessing by the processor, or in any other computing device languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The one or more processors 120 may be any conventional processors, suchas commercially available CPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor, such as a field programmable gate array(FPGA). Although FIG. 1 functionally illustrates the processor(s),memory, and other elements of computing device 110 as being within thesame block, it will be understood by those of ordinary skill in the artthat the processor, computing device, or memory may actually includemultiple processors, computing devices, or memories that may or may notbe stored within the same physical housing. For example, memory may be ahard drive or other storage media located in a housing different fromthat of computing device 110. Accordingly, references to a processor orcomputing device will be understood to include references to acollection of processors or computing devices or memories that may ormay not operate in parallel.

Computing device 110 may have all of the components normally used inconnection with a computing device such as the processor and memorydescribed above, as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen, a small LCD touch-screen or any otherelectrical device that is operable to display information). In thisexample, the vehicle includes an internal electronic display 152. Inthis regard, internal electronic display 152 may be located within acabin of vehicle 100 and may be used by computing device 110 to provideinformation to passengers within the vehicle 100.

In one example, computing device 110 may be an autonomous drivingcomputing system incorporated into vehicle 100. The autonomous drivingcomputing system may capable of communicating with various components ofthe vehicle as needed in order to control the vehicle in fullyautonomous (without input from a driver) as well as semiautonomous (someinput from a driver) driving modes.

As an example, FIG. 2 depicts an interior design of a vehicle havingautonomous, semiautonomous, and manual (continuous input from a driver)driving modes. In this regard, the autonomous vehicle may include all ofthe features of a non-autonomous vehicle, for example: a steeringapparatus, such as steering wheel 210; a navigation display apparatus,such as navigation display 215 (which may be a part of electronicdisplay 152); and a gear selector apparatus, such as gear shifter 220.The vehicle may also have various user input devices 140 in addition tothe foregoing, such as touch screen 217 (again, which may be a part ofelectronic display 152), or button inputs 219, for activating ordeactivating one or more autonomous driving modes and for enabling adriver or passenger 290 to provide information, such as a navigationdestination, to the computing device 110.

Returning to FIG. 1 , when engaged, computer 110 may control some or allof these functions of vehicle 100 and thus be fully or partiallyautonomous. It will be understood that although various systems andcomputing device 110 are shown within vehicle 100, these elements may beexternal to vehicle 100 or physically separated by large distances.

In this regard, computing device 110 may be in communication varioussystems of vehicle 100, such as deceleration system 160, accelerationsystem 162, steering system 164, signaling system 166, navigation system168, positioning system 170, and perception system 172, such that one ormore systems working together may control the movement, speed,direction, etc. of vehicle 100 in accordance with the instructions 134stored in memory 130. Although these systems are shown as external tocomputing device 110, in actuality, these systems may also beincorporated into computing device 110, again as an autonomous drivingcomputing system for controlling vehicle 100.

As an example, computing device 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computingdevice 110 in order to control the direction of vehicle 100. Forexample, if vehicle 100 configured for use on a road, such as a car ortruck, the steering system may include components to control the angleof wheels to turn the vehicle. Signaling system 166 may be used bycomputing device 110 in order to signal the vehicle's intent to otherdrivers or vehicles, for example, by lighting turn signals or brakelights when needed.

Navigation system 168 may be used by computing device 110 in order todetermine and follow a route to a location. In this regard, thenavigation system 168 and/or data 132 may store map information, e.g.,highly detailed maps identifying the shape and elevation of roadways,lane lines, intersections, crosswalks, speed limits, traffic signals,buildings, signs, real time traffic information, vegetation, or othersuch objects and information.

Further, the positioning system 170 may also include other devices incommunication with computing device 110, such as an accelerometer,gyroscope or another direction/speed detection device to determine thedirection and speed of the vehicle or changes thereto. By way of exampleonly, an acceleration device may determine its pitch, yaw or roll (orchanges thereto) relative to the direction of gravity or a planeperpendicular thereto. The device may also track increases or decreasesin speed and the direction of such changes. The device's provision oflocation and orientation data as set forth herein may be providedautomatically to the computing device 110, other computing devices andcombinations of the foregoing.

The perception system 172 may include one or more components fordetecting and performing analysis on objects external to the vehiclesuch as other vehicles, obstacles in the roadway, traffic signals,signs, trees, etc. Characteristics such as colors and words may also bedetected by one or more components of the perception system 172. Forexample, the perception system 172 may include lasers, sonar, radar, oneor more cameras, or any other detection devices which record data whichmay be processed by computing device 110. In the case where the vehicleis a small passenger vehicle such as a car, the car may include a lasermounted on the roof or other convenient location as well as othersensors such as cameras, radars, sonars, and additional lasers. Thecomputing device 110 may control the direction and speed of the vehicleby controlling various components. By way of example, if the vehicle isoperating completely autonomously, computing device 110 may navigate thevehicle to a location using data from the detailed map information andnavigation system 168. Computing device 110 may use the positioningsystem 170 to determine the vehicle's location and perception system 172to detect and respond to objects when needed to reach the locationsafely. In order to do so, computing device 110 may cause the vehicle toaccelerate (e.g., by increasing fuel or other energy provided to theengine by acceleration system 162), decelerate (e.g., by decreasing thefuel supplied to the engine or by applying brakes by deceleration system160), change direction (e.g., by turning the front or rear wheels ofvehicle 100 by steering system 164), and signal such changes (e.g., bylighting turn signals of signaling system 166).

FIG. 3 is an example of detailed map information 300 for a section ofroadway including an intersection 302. In this example, the detailed mapinformation 300 includes information identifying the shape, location,and other characteristics of lane lines 310, 312, 314, 316 trafficsignals 320, 322, 324, 326, crosswalks 330, 332, 334, 336. Although theexamples herein relate to a simplified three-state traffic signals,other types of traffic signals, such as those for right or left turnonly lanes may also be used.

In addition, the detailed map information may include a network ofrails, including rails 340, 342, 344, which provide the vehicle'scomputer with guidelines for maneuvering the vehicle so that the vehiclefollows the rails and obeys traffic laws. As an example, a vehicle'scomputer may maneuver the vehicle from point A to point B (twofictitious locations not actually part of the detailed map information)by following rail 340, transitioning to rail 342, and subsequentlytransitioning to rail 344 in order to make a left turn at intersection302.

Positioning system 170 may be used by computing device 110 in order todetermine the vehicle's relative or absolute position on a map or on theearth. For example, the position system 170 may include a GPS receiverto determine the device's latitude, longitude and/or altitude position.Other location systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the vehicle mayinclude an absolute geographical location, such as latitude, longitude,and altitude as well as relative location information, such as locationrelative to other cars immediately around it which can often bedetermined with less noise that absolute geographical location.

FIG. 4 is an example external view of vehicle 100 described above. Asshown, various components of the perception system 172 may be positionedon or in the vehicle 100 in order to better detect external objectswhile the vehicle is being driven. In this regard, one or more sensors,such as laser range finders 410 and 412 may be positioned or mounted onthe vehicle. Sensors of the same or different type may be placed on theside mirrors or along the sides of the vehicle, such as sensors 430 and432, to better detect objects on either side of vehicle 100. As anexample, the one or more computing devices 110 may control laser rangefinder 410, e.g., by rotating it 180 degrees. In addition, theperception system may include one or more cameras 420 mounted internallyon the windshield of vehicle 100, or elsewhere, to receive and analyzevarious images about the environment. Other detection devices, such assonar, radar, GPS, etc., may also be positioned in a similar manner. Thedetection devices may be used to track the position, size, and velocityof external objects, such as other vehicles or pedestrians, in thevicinity of the vehicle 100.

The one or more computing devices 110 may also include features such astransmitters and receivers that allow the one or more devices to sendand receive information to and from other devices. For example, the oneor more computing devices may determine information about the currentstatus of a traffic signal light as well as information about when thestatus of the traffic signal light changes (from green to yellow to redto green). The one or more computing devices may send this informationto other computing devise associated with other vehicles. Similarly, theone or more computing devices may receive such information from othercomputing devices. For example, the one or more computing devices mayreceive information about the current status of a traffic signal lightas well as information about when the status of the traffic signal lightchanges from one or more other computing devices associated with otherautonomous or non-autonomous vehicles. As another example, the one ormore computing devices may receive such information with devicesassociated with the traffic signal lights. In this regard, some trafficsignal lights may include transmitters that send out information aboutthe current status of a traffic signal light as well as informationabout when the status of the traffic signal light changes.

This information may be sent and received via any wireless transmissionmethod, such as radio, cellular, Internet, World Wide Web, intranets,virtual private networks, wide area networks, local networks, privatenetworks using communication protocols proprietary to one or morecompanies, Ethernet, WiFi and HTTP, and various combinations of theforegoing. Such communication may be facilitated by any device capableof transmitting data to and from other computers, such as modems andwireless interfaces.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

As noted above, a vehicle's one or more computing devices may maneuverthe vehicle using the various systems described above. For example, FIG.5 depicts a section of roadway 500 including an intersection 502.Autonomous vehicle 100 is approaching intersection 502 and may becontrolled in an autonomous driving mode.

In this example, intersection 502 corresponds to the intersection 302 ofthe detailed map information 300. In this regard, lane lines 510, 512,and 514 correspond to the shape, location, and other characteristics oflane lines 310, 312, and 314, respectively. Similarly crosswalks 530,532, 534, and 536 correspond to the shape, location, and othercharacteristics of crosswalks 330, 332, 334, and 336, respectively andtraffic signal 526 corresponds to the shape, location, and othercharacteristics of traffic signal 326. For simplicity, only a singletraffic signal 526 is shown, though other traffic signals correspondingto the shape, location, and other characteristics of traffic signals320, 322, and 324 may also exist.

Autonomous vehicle 100 may detect surrounding objects and track theirmovements. In addition, autonomous vehicle 100 may identify whether aparticular object is a mobile object, meaning that the object is capableof moving. Autonomous vehicle 100 may also identify whether a mobileobject is currently stationary. For example, autonomous vehicle 100 mayuse laser range finder 410, as well as other sensors and cameras, todetect vehicles 520 and 530, to identify those vehicles as automobiles,and to determine that the vehicles 520 and 530 are currently stationary.

In accordance with one aspect of the disclosure, autonomous vehicle 100may also determine the type of stationary state of the detected mobileobjects. For example, autonomous vehicle 100 may identify vehicles 520and 530 as being automobiles that are in either a short-term stationarystate or a long-term stationary state. In accordance with one aspect,the short-term stationary state may indicate that the object is merelystopped in the flow of traffic and will likely soon begin moving, whilethe long-term stationary state may indicate that the automobile isparked in a parking spot and will likely not begin moving with trafficin the near future.

To determine whether a vehicle is parked, an autonomous vehicle maydetermine whether detected vehicles have any visible indicia associatedwith a particular type of stationary state (e.g., parked). Theautonomous vehicle may use any number of visible indicia by determiningwhether each detected indicium weighs in favor of the vehicle beingparked or not. Lights such as hazard lights, emergency vehicle lights,construction warning lights, turn signals, brake lights, and reverselights may be detected and identified. For example, as autonomousvehicle 100 approaches vehicle 530, it may detect, using one or more ofthe sensors or cameras described above, that door 532 of vehicle 530 iscurrently open. In addition, autonomous vehicle 100 may detect thathazard lights 534 of vehicle 530 are flashing.

The autonomous vehicle may also identify one or more traffic controlfactors relating to the detected vehicles. These factors may includelocation of a vehicle, type of roadway, type of intersection, lane oftravel, location of parking spaces, or other traffic pattern in whichthe vehicle is currently positioned. To determine the location of thevehicle, the autonomous vehicle may compare the detected stationaryvehicles with map data. For example, as autonomous vehicle 100 maydetermine that vehicle 530 is positioned near curb 540.

The location of the vehicle in relation to the map data may then be usedto determine the type of roadway, intersection, or lane of travel inwhich the stationary vehicle is currently located. In addition, the mapdata may include the location of parking spaces and no park zones, thecolor of the curb, city ordinances regarding parking (e.g., whetherblocking one's own driveway is legal), or other information related toparking. This information included in the map data may be associatedwith a vehicle based on its determined location.

If the autonomous vehicle determines that one or more detected vehiclesare located in a designated parking space, the autonomous vehicle maydetermine that the vehicle is in a parked state. These detected vehiclesmay be labeled as parked in the system. Designated parking spaces may beparking spaces or parking lots delineated on the map data, but may alsoinclude areas where curbside parking is permitted. For example, vehiclesthat are close to a curb and also outside a specific distance from anintersection may be labeled as parked since they are likely pulled overand parked in a curbside parking area. The acceptable distance away froman intersection to park ranges depending on the layout of theintersection. Many distances may fall between 50 and 100 feet, but mayalso be shorter or longer. Various indicators may be used to determinewhat the acceptable distance is for a particular intersection; forexample, where a solid line delineating a shoulder becomes a dotted lineand where the curb color indicates parking is not permitted. Relevantcity ordinances may also be referenced by the system to determinewhether the area where the vehicles are stopped is a parallel parkingarea. The ordinances may determine parking based on factors such as theday of the week and the time of day.

As shown in FIG. 5 , an autonomous vehicle 100 may determine thatvehicle 530 is positioned near curb 540 an acceptable distance away fromintersection 502. It may also be determined that a city ordinancepermits parallel parking on roadway 500 on weekends and that today is aSaturday. Based on this determination, vehicle 530 may be labeled asparked in the system. However, a vehicle stopped in a shoulder or bikelane close to an intersection may be preparing to turn onto the crossstreet or otherwise likely to move again in a short period of time. Forexample, as autonomous vehicle 100 approaches intersection 502, it maydetermine that vehicle 520 is currently stationary and that vehicle 520is positioned just outside of intersection 502. Because vehicle 520 isnot an acceptable distance from the intersection 502, it may not belabeled as parked at this instance. Additionally, a vehicle stopped inroadway 500 on a weekday also would not initially be labeled as parkedbecause no parking is permitted at that time. In such a case, theacceptable distance may be set as the length of the roadway 500 onweekdays so that essentially there is no acceptable distance away fromthe intersection 502 a vehicle may be parked. Vehicles stopped where noparking is permitted along a roadway are likely only temporarilystopped, and therefore the autonomous vehicle initially may not labelsuch vehicles as parked.

The autonomous vehicle may also label one or more of the detectedvehicles as being in a parked state after they display characteristicsof a parked vehicle for a period of time. In one embodiment, a vehiclemay first be marked as possibly parked if it is not in a designatedparking space as discussed above, but has other characteristics of aparked vehicle at that point in time. Afterwards, if the vehicle remainspossibly parked for a set amount of time, it may be labeled as a parkedvehicle. For example, a vehicle may be labeled as parked if at least twoof the following conditions are met for a length of time as determinedbased on perception system data and the map data of the detected area.The conditions may include (1) being within a certain distance from aside of a road; (2) being a certain distance away from an intersection;(3) being stationary for a certain length of time; (4) having or lackingspecific visual indicia; (5) facing a certain angle in relation to theside of a road; (6) having a person exit the vehicle from the driver'sside; (7) leaving a certain amount of space on a side of the vehicle forpassing vehicles; (8) being at a type of intersection that does notrequire slowing or stopping; (9) being at a location where it is legalto park; (10) being in a group with other stationary vehicles that arestopped within a certain distance and at the certain angle from the sideof a road; and (11) having no significant changes in position asdetermined by the detection devices. It should be understood that theseconditions are examples, and not an exclusive list of conditions.

(1) Being within a certain distance from a side of the road. If avehicle is stopped close to the side of a road, then it is probablyparallel parked. Generally, the closer to the curb the detected vehicleis the more likely that the detected vehicle is parked. Depending on thesize and configuration of the road as determined based on the map data,the threshold distance required for satisfying this condition may vary.For example, if the road is a wide, multi-lane road, a vehicle beingwithin 2 to 3 feet of the curb may satisfy this condition. Whereas ifthe road is a narrow, single-lane road, the proper range where thiscondition would be met may be 1 foot or less. Additionally, if the roadhas an unpaved shoulder, the threshold distance may be a distance may beone that places a vehicle within the unpaved portion.

(2) Being a certain distance away from an intersection. Being a certaindistance away from the intersection also raises the probability that avehicle is parked. Vehicles stopped close to an intersection are mostlikely preparing to enter the intersection, but are required to stopbecause of some other reason. In addition, as discussed previously,parallel parking on along a street is often only permitted a specifieddistance away from the intersection. Therefore, the threshold distancefor a particular location may depend on the type of intersection or thelocal laws governing the road. The type of intersection may include thetype of traffic control at the intersection, if any. In some examples,the threshold may be 50 feet and in others it may be 100 feet. In yetother examples the threshold may be shorter, longer, or somewhere inbetween. As shown in FIG. 5 , vehicle 530 may be stopped outside thethreshold distance and therefore satisfies this condition, while vehicle520 may be within the threshold distance. This condition may not be usedin the determination in certain roadways. For example, in someresidential streets, cars may be parked right at the edge of theintersection. On these streets, this condition may not be applied in thedetermination of whether detected vehicles are parked.

(3) Being stationary for a certain length of time. Regarding the periodof time a vehicle has remained stationary, the longer the vehicle hasbeen stopped, the more likely the vehicle is parked. The autonomousvehicle may access data for the stationary vehicles from the perceptionsystem to determine whether the vehicle has been stationary not only atthe present moment, but for an extended period of time prior. Dependingon the speed the vehicle was traveling prior to stopping, the thresholdlength of time required to satisfy this condition may vary. For example,the threshold for factoring in favor of being parked is longer when acar was previously traveling at a high speed than a low speed. By way ofexample, to be considered likely parked, cars previously traveling athigh speed may need to be stationary for between 10 and 30 seconds ormore, whereas cars previously traveling at a low speed need only bestationary for around 5 seconds.

(4) Having or lacking specific visual indicia. The presence or absenceof certain visual indicia may be another indication that a stationaryvehicle is parked. For example, the presence of emergency vehicle lightsor construction warning lights may indicate that a vehicle is parked.However, if hazard lights, brake lights, reverse lights, or turn signalsof the vehicle are on, the vehicle is probably not parked because such asignal often indicates that a vehicle may soon begin moving again. Itfollows that the absence of such lights may weigh in favor of a vehiclebeing parked. Therefore, if the computing devices detect emergencyvehicle lights or other lights indicative of a parked vehicle, then thiscondition may be satisfied. Additionally, the absence of brake lights orother lights considered indicative of a vehicle in the flow of trafficmay also satisfy this condition. Vehicle 530 has hazard lights 534 on,so based solely on the hazard lights as visual indicia, this conditionis not satisfied. However, other visual indicia besides lights may betaken into account; for example, the position of a vehicle's doors. InFIG. 5 , driver's door 532 of vehicle 530 is open. This visual indiciummay be enough to satisfy this condition.

If hazard lights, brake lights, reverse lights, or turn signals aredetected, the autonomous vehicle may then determine whether or not avehicle is in fact stalled, and therefore not likely to move in the nearfuture. A method such as the one described in U.S. patent applicationSer. No. 14/523,253, which is incorporated by reference herein, may beused for this purpose.

(5) Facing a certain angle in relation to the side of a road. Facing acertain angle in relation to the side of a road may indicate that avehicle is parked in a parallel parking spot, an angled parking spot, ora head-in parking spot, even if it is not a delineated parking spot inthe map data. The headings of the stationary vehicles in relation to thestreet may be compared with the direction of the street and the positionof the curb. If the car heading is parallel, orthogonal, or at aparticular angle to the curb (e.g., approximately 30 degrees), thesystem may consider this condition satisfied because they correspond toparallel parking, head in parking, or angled parking, respectively. Bothvehicle 520 and 530 have headings that are parallel to the roadway 500.For a curved road, the curvature is taken into account to increase bothheading and distance tolerances. In other words, the range of headingsthat would satisfy this condition may be greater for roads with greatercurvatures. However, specifically with respect to cul-de-sacs, roadswith large shoulders, and other street layouts in which vehicles areoften parked with no consistent heading, this heading factor andtolerance may not be considered in the determination of whether or not avehicle is parked.

(6) Having a person exit the vehicle from the driver's side. Theautonomous vehicle may consider the behavior and location of peoplearound a stationary vehicle to determine whether or not the vehicle isparked. This condition for the identification of parked vehicles may besatisfied when one or more people located around the vehicle behave inspecified ways. For example, if a person exits out of the vehicle fromthe driver's side, this condition may be satisfied. If a person hadexited vehicle 530, leaving door 532 ajar, this condition would besatisfied. On the other hand, if a person exits from the passenger sideor enters the vehicle, this condition may not be satisfied. If a personis in or next to a crosswalk in front of the stationary vehicle, thiscondition also may not be met.

(7) Leaving a certain amount of space on a side of the vehicle forpassing vehicles. Another condition that may be used to determine parkedvehicles is having a minimum amount of space on the non-curb side of thevehicle. Using map data about the lane width and presence of otherboundaries of the road, the system may determine that there is enoughspace on the non-curb side of the vehicle for another vehicle to passby. The system may allow for a slight breach of the center of the road(often marked with a lane marker or yellow boundary) depending on thetype of marker or boundary (e.g., no more than half the width of the carfor a solid yellow boundary) in areas of the map, such as residentialareas, where the practice is common. The threshold for the minimumamount of space on the side of a stopped vehicle closer to the roadwaymay therefore vary based on the width of the road, the type of road orlocation, and the average size of vehicles that travel that road. If thespace on the non-curb side of the stationary vehicle meets thethreshold, the vehicle is probably parked. For example, vehicle 530 inFIG. 5 is far enough to the right side of the roadway 500 to leave roomon the other side for most vehicles to pass. On the other hand, vehicle520 is parked further in the middle of roadway 500 and therefore may notleave enough room for another vehicle to pass.

(8) Being at a type of intersection that does not require slowing orstopping. A vehicle stopped near an intersection that does not requirestopping or slowing is probably parked. In other words, determiningwhether a stationary vehicle is parked may also include determining thetype of intersection by which it is stopped. In particular, the systemmay use the map data to identify the type of intersection near thestopped vehicle. For example, if the intersection is one with a stopsign, stop light, yield, or other type of intersection that may requirea vehicle to stop before continuing in the flow of traffic, the detectedvehicle is likely not parked. On the other hand, if the intersection isunregulated or if the traffic light is green, the stationary vehicle ismore likely parked and may satisfy this condition. This condition mayfurther require that no vehicles be crossing the intersection via thecross street to ensure that a vehicle is not stopped at the intersectionmerely to let another vehicle clear the intersection before drivingthrough it.

However, this condition may not be considered if the stationary vehicleis a certain distance away from the intersection because the movement ofa vehicle far away from an intersection would not be greatly affected bythe type of intersection. As such, in one embodiment, it may first bedetermined by the perception system whether a vehicle is a certaindistance away from the intersection. This threshold distance may bedifferent from the threshold distance related to the condition of beinga certain distance away from an intersection mentioned above. If thevehicle is within the threshold distance, then the system may evaluatewhether this type of intersection condition is satisfied. Vehicle 520may be within the threshold distance for roadway 500. The perceptionsystem may then detect that the intersection has a traffic light 526that regulates the traffic in the relevant lane of travel. In addition,the system may detect that the traffic light is currently red. As such,vehicle 520 does not satisfy this condition.

(9) Being at a location where it is legal to park. The legality ofparking in a specified area may be determined by detecting the presencespecific features of the roadway and by referring to the city ordinancesfrom the map data in relation to the detected features. Features may bedetected by the perception system of the autonomous vehicle and mayinclude fire hydrants, no parking signs, driveways, curbs painted aparticular color (e.g., red), and bike lanes. For example, some citiesallow for parking in the bike lanes after a certain time of day oncertain days of the week. Therefore, if a vehicle is detected stopped ina bike lane after 5:00 pm on a Saturday, when parking is permittedthere, the condition of being at a location where it is legal to parkmay be satisfied.

(10) Being in a group with other stationary vehicles that are stoppedwithin a certain distance and at the certain angle from the side of aroad. The detection of other nearby vehicles may also factor in to thedetermination that the vehicle is parked. If more than one car isstopped at a similar heading along the same curb in the proximity of thestationary vehicle, the uniformity of their positioning may indicatethat it is likely that the cars are parked. As such, when a group ofsuch vehicles are detected by the perception system of the autonomousvehicle, this condition may be satisfied regarding the vehicles in thegroup. Additionally or alternatively, if it is detected that severalvehicles are navigating around a stationary vehicle, the autonomousvehicle may determine that the stationary vehicle is likely parked.Otherwise, if there is no other cars similarly stopped in the area or ifseveral cars in the flow of traffic are stopping behind a stationaryvehicle, this condition may not be met.

(11) Having no significant changes in position as determined by thedetection devices. Even if a stationary vehicle was in motion shortprior to being stopped, it may still be considered parked if the motionwas insignificant. Insignificant motions include small changes inposition, extremely low speed movements, and changes in position thatare not likely to affect the flow of traffic. For example, theperception system may have observed a change in the observed extent of avehicle within a short time period prior to the vehicle becomingstationary. If this change was smaller and/or happened slower than a setthreshold, it may be deemed insignificant and therefore this conditionmay be satisfied. However, if shortly before a vehicle stopped moving,the sensors of the autonomous vehicle went from detecting only the rearportion of a stationary vehicle to, a second later, detecting part ofthe side portion of the vehicle, this may be considered a significantchange and the condition may be unmet. A vehicle displaying this sort ofchange be backing up out of a spot or jutting out more into the roadway.Additionally or alternatively, the sensors of the autonomous vehicle,such as a radar, may measure the relative velocity directly and detectany significant changes using the measurement.

One, more than one, or none of the aforementioned conditions may be usedby an autonomous vehicle to determine if the detected vehicles arepossibly parked. The conditions mentioned may also be formulateddifferently than how they are described above. For example, in someexamples, one of the aforementioned conditions may be implemented asmore than one condition.

If, having evaluated whether stationary vehicles satisfy the conditionsof parked vehicles, the detected vehicles are determined to be possiblyparked, the perception system tracks these vehicles for a short timeperiod to ensure that the state of the vehicle and its surroundingsremains consistent with the original determination. If a change isdetected in the state of the vehicle and/or its surroundings within thetime period, the system may again determine whether or not the detectedvehicle is still parked, possibly parked, or unparked based on the newconditions. In one embodiment, a vehicle may be considered unparked iftwo or more conditions of a parked vehicle are no longer satisfied afterthe changes are taken into account. Detecting significant changes in thesize and shape of the detected portion of a vehicle as well as the speedof a vehicle is particularly important as these changes are stronglyindicative of a vehicle in the process of unparking. The threshold forthese changes to be considered significant may be small in order toallow for the proper level of sensitivity in the system to thesechanges.

In some examples, the time period required before a vehicle is deemed asparked is dependent on the speed of the vehicle prior to it stopping.The faster the vehicle was traveling before, the longer the timerequired before the system meets the designated threshold for labelingthe vehicle as parked. Once the requisite time period has elapsedwithout a change to the possibly parked status, the detected vehicle maybe labeled as parked.

After the detected vehicle is labeled as parked, the system continues totrack the vehicle with its sensors and detection devices to detect anychanges in the visual indicia or the conditions. If a certain amount ofchanges occur with respect to a parked vehicle, the parked label may beremoved from the detected vehicle. In one example, this may bedetermined based on a likelihood value of whether the vehicle isunparking. In an alternate example, unparking may be determined based onan evaluation of the conditions mentioned previously with respect toidentifying parked vehicles. Any change in these conditions mayconstitute a change in condition that would require a reevaluation ofwhether or not the vehicle is still parked. As discussed previously, theperception system may be calibrated to be particularly sensitive tochanges to the size and shape of the detected portion of a vehicle andthe speed of a vehicle. In addition, the perception system may accessthe parking status histories of the detected vehicles and weigh thechanges in conditions based on the histories. For example, if thedetected vehicle was first labeled as possibly parked for a period oftime, then labeled as parked for a second or two, a relatively smalldetected change in condition may not be enough to change the label ofthe vehicle to unparked. A vehicle with such a history that starts tomove is likely adjusting its parking job.

However, if the likelihood value of being an unparking vehicle meets aspecified threshold or if the vehicle no longer satisfies the conditionsof a parked vehicle, the parked label may therefore be removed from thevehicle in the system, and the autonomous vehicle may be directed toreact accordingly; for example, stop to let the vehicle out of the spot.Because vehicle 530 satisfied two or more of the aforementionedconditions (and presumably for the requisite amount of time), autonomousvehicle 100 may navigate around as shown in FIG. 6 . The aforementionedtolerances and thresholds may be optimizing over large datasetscontaining parked and moving vehicles either manually or via one or morecomputing devices.

The autonomous vehicle may continuously detect the vehicles in itsvicinity and determine which vehicles in the detected set of vehiclesare stationary at a given moment in time and, from the continuouslyupdated set of stationary vehicles, the autonomous vehicle may determineparked and which are unparked.

FIG. 7 is an example flow diagram 700 which depicts some of the aspectsdescribed above which may be performed by the one or more computingdevices, such as one or more computing devices 110, as vehicle 100 isbeing autonomously controlled in accordance with a control strategy.Flow diagram 700 may also be performed for each vehicle detected byvehicle 100. In this example, autonomous vehicle 100 may detect anothervehicle at block 710. It is determined whether the detected vehicle isstationary at block 720. If the detected vehicle is not stationary, itis marked as unparked at block 722.

If the detected vehicle is stationary, then the autonomous vehicle 100may detect any visible indicia from the stationary vehicle at block 730and identify traffic control factors related to the stationary vehicleat block 740. At block 740, it may be determined whether the stationaryvehicle is in a parking space. If it is in a parking space, thenautonomous vehicle 100 may label it as parked at block 752.

If it is not in a parking space, then it may be determined if thestationary vehicle satisfies two or more conditions indicative of beingparked for a set amount of time. If two or more conditions are satisfiedfor the specified amount of time, then autonomous vehicle 100 may labelthe stationary vehicle as parked at 752. If two or more conditions arenot satisfied, then autonomous vehicle 100 may label the stationaryvehicle as unparked.

Based on the whether the detected vehicle is identified as parked orunparked, autonomous vehicle 100 may be controlled in accordance with acontrol strategy. At block 780, it may be determined whether any changesare detected in the visual indicia, size, shape, velocity, or othercharacteristics of the detected vehicle or in the traffic controlfactors. If there are any significant changes, then the detected vehicleis reanalyzed by starting from block 720 again. If there are nosignificant changes, then the process ends. In this way, autonomousvehicle 100 may update the detected vehicle's stationary statedesignation, as well as the autonomous vehicle's control strategy.

Some examples above refer to ‘set of vehicles’ to illustrate how anautonomous vehicle can handle a busy environment with low latency.However, the technique(s) could be used even when the environmentincludes a single vehicle of course (e.g., a set of one or morevehicles). Additionally, although an autonomous vehicle is discussedherein, the methods and systems may be applied in a vehicle controlsystem of a non-autonomous vehicle (e.g., as part of a driverassistance, cruise control, or safety system).

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter as defined by the claims, theforegoing description of exemplary embodiments should be taken by way ofillustration rather than by way of limitation of the subject matter asdefined by the claims. It will also be understood that the provision ofthe examples described herein (as well as clauses phrased as “such as,”“e.g.”, “including” and the like) should not be interpreted as limitingthe claimed subject matter to the specific examples; rather, theexamples are intended to illustrate only some of many possible aspects.

The invention claimed is:
 1. A method comprising: detecting, by one ormore computing devices, a first vehicle in an environment of a secondvehicle, the second vehicle being capable of driving autonomously;detecting, by the one or more computing devices, a position of the firstvehicle; identifying, by the one or more computing devices, the firstvehicle as parked when the detected vehicle is at least one of (i)determined to be located in a designated parking space based on thedetected position, and (ii) determined to display one or morecharacteristics of a parked vehicle for a first length of time based onat least the detected position relative to one or more traffic controlfactors; and controlling, by the one or more computing devices, thesecond vehicle to pass the first vehicle when the first vehicle isidentified as parked.
 2. The method of claim 1, wherein identifying thefirst vehicle as parked includes: comparing the detected position of thefirst vehicle with map data; and determining the detected position ofthe first vehicle is corresponds to a designated parking space in themap data.
 3. The method of claim 1, wherein identifying the firstvehicle as parked includes: determining the first vehicle is in adesignated parking space based on the detected position; and determininga type of parking space the designated parking space is.
 4. The methodof claim 1, wherein identifying the first vehicle as parked includes:tracking the one or more characteristics displayed by the first vehiclefor the first length of time; and determining the first vehicle isparked when the one or more characteristics remains unchanged during thefirst length of time.
 5. The method of claim 1, wherein identifying thefirst vehicle as parked is based at least in part on a scheduled eventor a calendar event.
 6. The method of claim 1, wherein controlling thesecond vehicle to pass the first vehicle when the first vehicle isidentified as parked includes slowing down less than when the firstvehicle is not identified as parked.
 7. The method of claim 1, furthercomprising: determining, by the one or more computing devices, that thefirst vehicle is not parked; and controlling, by the one or morecomputing devices, the second vehicle based on the first vehicle beingpart of traffic flow.
 8. The method of claim 7, wherein controlling thesecond vehicle based on the first vehicle being part of the traffic flowincludes maneuvering the second vehicle in response to a possiblemovement of the first vehicle in the traffic flow.
 9. A systemcomprising: one or more sensors; and one or more computing devicesconfigured to: detect, using the one or more sensors, a vehicle in anenvironment of an autonomous vehicle, the vehicle having a detectedposition; identify the detected vehicle as parked when the detectedvehicle is at least one of: (i) determined to be located in a designatedparking space, and (ii) determined to display one or morecharacteristics of a parked vehicle for a first length of time based onat least the detected position relative to one or more traffic controlfactors; and control the autonomous vehicle to pass the detected vehiclewhen the detected vehicle is identified as parked.
 10. The system ofclaim 9, wherein the one or more computing devices are configured toidentify the detected vehicle as parked based on: a comparison of thedetected position of the detected vehicle with map data; and adesignated parking space in the map data that corresponds with thedetected position.
 11. The system of claim 9, wherein the one or morecomputing devices are configured to identify the detected vehicle asparked based on a type of parking space that the designated parkingspace is when the detected vehicle is in a designated parking space. 12.The system of claim 9, wherein the one or more computing devices areconfigured to identify the detected vehicle as parked based on the oneor more characteristics displayed by the detected vehicle that remainunchanged during the first length of time.
 13. The system of claim 9,wherein the one or more computing devices are configured to identify thedetected vehicle as parked based at least in part on a scheduled eventor a calendar event.
 14. The system of claim 9, wherein the one or morecomputing devices are configured to, when the detected vehicle isidentified as parked, control the autonomous vehicle to slow down lessthan when the detected vehicle is not identified as parked.
 15. Thesystem of claim 9, wherein the one or more computing devices are furtherconfigured to: determine that the detected vehicle is not parked; andcontrol the autonomous vehicle based on the detected vehicle being partof traffic flow.
 16. The system of claim 15, wherein the one or morecomputing devices are configured to control the autonomous vehicle basedon the detected vehicle being part of the traffic flow by maneuveringthe autonomous vehicle in response to a possible movement of thedetected vehicle in the traffic flow.
 17. A non-transitorycomputer-readable medium on which instructions are stored, theinstructions, when executed by one or more computing devices, cause theone or more computing devices to perform a method, the methodcomprising: detecting a first vehicle in an environment of a secondvehicle, the second vehicle being capable of driving autonomously;detecting a position of the first vehicle; identifying the first vehicleas parked when the detected vehicle is at least one of (i) determined tobe located in a designated parking space based on the detected position,and (ii) determined to display one or more characteristics of a parkedvehicle for a first length of time based on at least the detectedposition relative to one or more traffic control factors; andcontrolling the second vehicle to pass the first vehicle when the firstvehicle is identified as parked.
 18. The medium of claim 17, whereinidentifying the first vehicle as parked includes: comparing the detectedposition of the first vehicle with map data; and determining thedetected position of the first vehicle is corresponds to a designatedparking space in the map data.
 19. The medium of claim 17, whereinidentifying the first vehicle as parked includes: determining the firstvehicle is in a designated parking space based on the detected position;and determining a type of parking space the designated parking space is.20. The medium of claim 17, wherein identifying the first vehicle asparked includes: tracking the one or more characteristics displayed bythe first vehicle for the first length of time; and determining thefirst vehicle is parked when the one or more characteristics remainsunchanged during the first length of time.